ICU mortality prediction: A classification algorithm for imbalanced datasets

Abstract

Determining mortality risk is important for critical decisions in Intensive Care Units (ICU). The need for machine learning models that provide accurate patient-specific prediction of mortality is well recognized. We present a new algorithm for ICU mortality prediction that is designed to address the problem of imbalance, which occurs, in the context of binary classification, when one of the two classes is significantly under-represented in the data. We take a fundamentally new approach in exploiting the class imbalance through a feature transformation such that the transformed features are easier to classify. Hypothesis testing is used for classification with a test statistic that follows the distribution of the difference of two χ2 random variables, for which there are no analytic expressions and we derive an accurate approximation. Experiments on a benchmark dataset of 4000 ICU patients show that our algorithm surpasses the best competing methods for mortality prediction.